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High Level Trigger

L0. L1. L2. HLT. High Level Trigger. Trigger Accept/reject events Select Select regions of interest within an event Compress Reduce the amount of data required to encode the event as far as possible without loosing physics information Provide HLT-ESDs for online monitoring

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High Level Trigger

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  1. L0 L1 L2 HLT High Level Trigger • Trigger • Accept/reject events • Select • Select regions of interest within an event • Compress • Reduce the amount of data required to encode the event as far as possible without loosing physics information • Provide HLT-ESDs for online monitoring • Access to the results of the event reconstruction • Physics Requirements Dieter Roehrich UiB

  2. Detectors DAQ HLT Mass storage Physics Applications • Quarkonium spectroscopy • Dielectrons • Dimuons • Open Charm • Jets • Pileup removal in pp

  3. Quarkonium T. Vik, PhD thesis, Oslo, 2005 • Dielectrons • HLT task • Reject fake TRD triggers and reduce trigger rate by factor of more than 10 • Status • Fast TPC pattern recognition – done • Additional PID by dE/dx – done • Adaption of Kalman filter for HLT – done • Combined track fit TRD-TPC-ITS – in progess • To do • Emulate the TRD Global Tracking Unit (TRD tracklet merging and PID) • Dimuons • HLT task • Utilizing tracking chamber information and improving momentum resolution • Sharpening of pt-cut • Rejection factors: low pt-cut: 5, high pt-cut: 100 • Status • Complete simulation including cluster finder – done • Full scale prototype HLT farm (UCT) – done • FPGA cluster finder – in progress • FPGA interface – in progress

  4. Open charm • HLT task • Detection of hadronic charm decays: D0 K– + + • About 1 D0 per event (central Pb-Pb) in ALICE acceptance • After cuts • signal/event = 0.001 • background/event = 0.01 • Status • Detailed study of timing profile of offline algorithm - done • Adaption of ITS tracking to HLT and speed-up – done • Optimization of D0 finder – in progress • Combine HLT tracking and D0 algorithm – in progress • To do • estimate the efficiency for appling D0-offline-cuts online • extend study to D+, D*+

  5. Online • Available modules • TPC cluster finder (CF) • TPC track follower (TF) • Kalman fitter • TPC Hough transform tracker (1) • TPC Hough transform tracker (2) • TPC cluster deconvolution • TPC performance monitor • TPC dE/dx • TPC data compression (1) • TPC data compression (2) • ITS tracker • Dimuon cluster finder • Dimuon tracker • Jet cone finder • D0 finder • PHOS pulse shape analysis

  6. Tracking performance for CF/TF Tracking efficiency Momentum resolution Computing time: 13 sec per event (dn/dy=4000) on a 1kSPECInt machine A. Vestbø, PhD thesis, Bergen, 2004

  7. Tracking performance for Hough transform – version 1 • Gray-scale Hough transform • Image space: raw ADC counts • Transform space: circle parameters • Histogram increment: charge too CPU-time consuming A. Vestbø, PhD thesis, Bergen, 2004

  8.  slice of TPC sector Corresponding Hough Space Tracking performance for Hough transform – version 2 (1) • Linearized prehistoric Hough transform • Image space: conformal mapped cluster boundaries • Transform space: straight line parameters • Histogram increment: history of missing padrows, conditional Collaboration with the Offline group: Cvetan Cheshkov

  9. Tracking performance for Hough transform – version 2 (2) Tracking efficiency dN/dy=8000 dN/dy=6000 dN/dy=4000 dN/dy=2000 B=0.5T Cvetan Cheshkov

  10. Tracking performance for Hough transform – version 2 (3) • Momentum resolution • Pt/Pt=(1.8xPt+1.0)% (B=0.5T) • ()=6.1mrad • ()=5.5x10-3 • Computing time (1.3 kSpecInt machine) Cvetan Cheshkov

  11. ITS Clusterer clusters ITS Vertexer HLT TPC Tracker TPCtracks ITS Tracker ITS tracking (1) • Offline tracking • Modified offline code • Speed-up of up to a factor of 30 for some modules J. Belikov, C.Cheshkov

  12. ITS tracking (2) • Tracking efficiency TPC only (HT) ITS+TPC Fakes B=0.5T Comparable to offline J. Belikov, C.Cheshkov

  13. ITS tracking (3) • Impact parameter resolution Dominated by SPD -> ”offline” quality, i.e. 1GeV/c track: transverse impact parameter resolution = 60 microns J. Belikov, C.Cheshkov

  14. ITS tracking (4) • Computing time (1.3 kSPECInt PC) J. Belikov, C.Cheshkov

  15. D0 finder • Offline algorithm • Cut on impact parameter • calculate • Distance of closest approach • Invariant mass • Decay angle • Pointing angle • Timing results (0.3 kSPECInt PC)

  16. TPC Data Compression - Principle Standard loss(less) algorithms; entropy encoders, vector quantization ... - achieve compression factor ~ 2 (J. Berger et. al., Nucl. Instr. Meth. A489 (2002) 406) Data model adapted to TPC tracking Store (small) deviations from a model: (A. Vestbø et. al., to be publ. In Nucl. Instr. Meth. ) Cluster model depends on track parameters Tracking efficiency before and after comp. Relative pt-resolution before and after comp. Tracking efficiency Relative pt resolution [%] dNch/d=1000

  17. TPC Data Compression - Results Achieved compression ratios and corresponding efficiencies Compression factor: 10

  18. PHOS Data Compression • Data volume • 18k crystals • Occupancy: ~10% (min. bias Pb+Pb, E > 10 MeV) • 10 MHz sampling frequency • 128 samples per pulse • 2 channels per crystal • 10 bits per sample • Readout • all channels: 6 Mbyte/event • discard empty channels (after zero-suppresion): 0.6 Mbyte/event • Date rate • 2 kHz ’clean’ Pb+Pb interaction rate: 1.2 GByte/sec

  19. Gamma-2 fit Peak Method : Offline time reference at peak ( y’ =0 ) Slope Method:Offline time reference at max. slope ( y”=0 ) (both reference points are amplitude independent) PHOS Data Compression • Online pulse shape analysis • Fit amplitude -> energy • Fit time offset -> TOF • Peak method • Slope method

  20. HLT task in pp • TPC • event reconstruction • primary vertex • primary vertex tracks • secondary vertex tracks • ghost (non-vertex) tracks • ITS • SPD and SSD tracking • TRD, PHOS, ... • Full event reconstruction • data compression • pile-up rejection

  21. Pattern recognition scenario in pp • TPC tracking strategy • Cluster finder • Track follower (conformal mapping method) • First pass with vertex constraint • Second pass in order to improve efficiencies for low-pt and secondary tracks • input all unassigned clusters from the first pass • no vertex constrain is imposed on the track follower (conformal mapping done with respect to the first associated cluster on track) • Kalman filter for track extension into TRD and ITS • PID in TRD and TPC

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